Individual Puck Possessions Part I
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper we use puck and player tracking data from the 2023- 24 NHL season to study individual player possessions (focusing on 5v5 situations). We study metrics such as possession count, average and total possession duration, average and total distance travelled with the puck, and examine relationships between these metrics and traditional measures of success (i.e., goals, assists and points). A key finding is that individual offensive zone possession is strongly correlated with points (r = 0.70) and is moderately correlated with goals (r = 0.64), assists (r = 0.54), and shots on goal (r = 0.69). We also observe differences in individual possessions based on position (forwards versus defence), zone of play, and strength and large and statistically significant differences between top ranked players and league averages (across most possession metrics). Finally, we examine the benefits of our individual possession metrics and find that they are highly stable (so they are useful for predictions), able to differentiate players, and provide information not captured by existing metrics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it